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  • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
  • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
  • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
  • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
  • G01N33/57407—Specifically defined cancers
  • G01N33/5743—Specifically defined cancers of skin, e.g. melanoma
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  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
  • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
  • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
  • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
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  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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  • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
  • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
  • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
  • G01N33/5308—Immunoassay; Biospecific binding assay; Materials therefor for analytes not provided for elsewhere, e.g. nucleic acids, uric acid, worms, mites
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  • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
  • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
  • G01N33/58—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving labelled substances
  • G01N33/582—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving labelled substances with fluorescent label
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
  • A61K39/00—Medicinal preparations containing antigens or antibodies
  • A61K39/395—Antibodies; Immunoglobulins; Immune serum, e.g. antilymphocytic serum
  • A61K39/39533—Antibodies; Immunoglobulins; Immune serum, e.g. antilymphocytic serum against materials from animals
  • A61K39/3955—Antibodies; Immunoglobulins; Immune serum, e.g. antilymphocytic serum against materials from animals against proteinaceous materials, e.g. enzymes, hormones, lymphokines
• • A—HUMAN NECESSITIES
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  • A61P17/00—Drugs for dermatological disorders
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
  • A61P35/00—Antineoplastic agents
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  • C12Q2563/00—Nucleic acid detection characterized by the use of physical, structural and functional properties
  • C12Q2563/107—Nucleic acid detection characterized by the use of physical, structural and functional properties fluorescence
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  • C12Q2600/00—Oligonucleotides characterized by their use
  • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
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  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
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  • C12Q2600/00—Oligonucleotides characterized by their use
  • C12Q2600/118—Prognosis of disease development
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
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  • C12Q2600/00—Oligonucleotides characterized by their use
  • C12Q2600/158—Expression markers
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2800/00—Detection or diagnosis of diseases
  • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2800/00—Detection or diagnosis of diseases
  • G01N2800/56—Staging of a disease; Further complications associated with the disease
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2800/00—Detection or diagnosis of diseases
  • G01N2800/60—Complex ways of combining multiple protein biomarkers for diagnosis
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2800/00—Detection or diagnosis of diseases
  • G01N2800/70—Mechanisms involved in disease identification
  • G01N2800/7023—(Hyper)proliferation
  • G01N2800/7028—Cancer

Definitions

• the present invention relates to melanoma detection.
• melanoma is a common (-24-35/100,000 incidence - US), highly aggressive, skin cancer with an incidence that continues to rise. The most common, cutaneous melanomas, are associated with UV exposure and immune dysregulation. As a group, melanoma is known to carry the highest mutational burden (>10mutations/Mb). Major mutations include BRAF
• melanomas are addicted to MAPK pathway activation, regardless of whether tumors exhibit mutations in genes coding for proteins in this pathway. This provides the rationale for targeted therapy e.g., BRAF v600E agents, in this tumor. Other gain-of-function and loss-of-function mutations e.g., in RASopathy genes and amplification of cyclin Dl/cdk4 and/or mutation/loss of the tumor suppressor PTEN, also characterize the tumor. This makes melanoma one of the most aggressive and therapy-resistant cancers. [0005] Five-year survival rates range from 95-100% for stage I, 65-93% for stage II, to 41- 71% and 9-28% for stage III and IV, respectively. Surgery, immunotherapy and targeted therapies provide the basis for management, with chemotherapy and radiation as adjuncts.
• Blood-based factors include lactate dehydrogenase (LDH), detecting mutations in circulating tumor (ct) DNA, measurements of circulating tumor ceils (CTCs) and circulating mRNA.
• LDH lactate dehydrogenase
• CTCs circulating tumor ceils
• mRNA Circulating microNAs
• a 28-gene expression tool for melanoma has high sensitivity and specificity (>95%) for the detection of melanoma and can differentiate aggressive untreated disease from stable, treated disease.
• One aspect of the present disclosure relates to a method for detecting a melanoma in a subject in need thereof, comprising: (1) determining the expression level of at least 29 biomarkers from a test sample from the subject by contacting the test sample with a plurality of agents specific to detect the expression of the at least 29 biomarkers, wherein the at least 29 biomarkers comprise ATLL ATP6V0D, C1GRF21, CFLAR, CFLAR-AS 1, CHPl, DDX55, DMD, DNAJC9, ENOSFl, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAPl, LOC494127, LOC646471, LOH12CR, PBXIPl, R F5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, YY2, and at least one housekeeping gene, (2) normalizing the expression level of each of ATL1 ,
• the method further comprises treating the subject identified as having a melanoma with surgery or drug therapy.
• Another aspect of the present disclosure relates to a method for determining whether a melanoma in a subject is stable or progressive, comprising: (1) determining the expression level of at least 29 biomarkers from a test sample from the subject by contacting the test sample with a plurality of agents specific to detect the expression of the at least 29 biomarkers, wherein the at least 29 biomarkers comprise ATLl, ATP6V0D, C10RF21, CFLAR, CFLAR-AS1, CHPl, DDX55, DMD, DNAJC9, ENOSF I , FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAPl, LOC494127, LOC646471, LOH12CR, PBXIP l , RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, ⁇ 2, and at least one housekeeping gene, (2) normalizing the expression level of each of
• Another aspect of the present disclosure relates to a method for evaluating the extent of surgical resection in a subject having a melanoma, comprising: (1) determining the expression level of at least 29 biomarkers from a test sample from the subject after the surgical resection by contacting the test sample with a plurality of agents specific to detect the expression of the at least 29 biomarkers, wherein the at least 29 biomarkers comprise ATLl, ATP6V0D, C10RF21, CFLAR, CFLAR-ASl , CHP1 , DDX55, DMD, DNAJC9, ENOSFl, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LQH12CR, PBXIPl , RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, ⁇ 2, and at least one housekeeping gene
• the report further identifies that the risk of melanoma recurrence is high when the normalized expression level is equal to or greater than the third predetermined cutoff value or identifies that the risk of melanoma recurrence is low when the normalized expression level is below the third predetermined cutoff value.
• Yet another aspect of the present disclosure relates to a method for determining a response by a subject having a melanoma to a therapy, comprising: (1 ) determining a first expression level of at least 28 biomarkers from a first test sample from the subject at a first time point by contacting the first test sample with a plurality of agents specific to detect the expression of the at least 28 biomarkers, wherein the 28 biomarkers comprise ATL1, ATP6V0D, C10RF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF 1 , FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1 , LOC494127, LOC646471, LOH12CR, PBXIP1, R F5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2; (2) determining a first expression
• the first time point is prior to the administration of the therapy to the subject. In some embodiments, the first time point is after the administration of the therapy to the subject.
• the therapy comprises an immunotherapy or a targeted therapy (e.g., a BRAF inhibitor).
• the at least one housekeeping gene is selected from the group consisting of ALG9, SEPN, YWHAQ, VP S37 A, PRRC2B, DOPEY2, NDUFB11, ND4, MRPL19, PSMC4, SF3A1 , PUM1 , ACTB, GAPE), GUSB, RPLPO, TFRC, MORF4L1 , 18S, PPIA, PGKI, RPL13A, B2M, YWHAZ, SDHA, HPRTl, TOX4, and TPT1.
• the at least one housekeeping gene comprises TOX4 and TPT1 ,
• the normalized expression level is obtained by: (1) normalizing the expression level of each of ATLl, ATP6V0D, C10RF21 , CFLAR, CFLAR-AS1, CH l, DDX55, DMD, DNAJC9, ENOSFI, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIPl, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2 to the expression level of TOX4, thereby obtaining a first normalized expression level of each of ATLl , ATP6V0D, C10RF21 , CFLAR, CFLAR-AS1, CHPl, DDX55, DMD, DNAJC9, ENOSFI, FANCL, HJURP, HLA-DOA, HLA-DR
• the method can have a specificity, sensitivity, and/or accuracy of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. In some embodiments, the method has a sensitivity of greater than 90%. In some embodiments, the m ethod has a specificity of greater than 90%.
• the biomarker is RNA, cDNA, or protein. When the biomarker is RNA, the RNA can be reverse transcribed to produce cDNA, and the produced cDNA expression level is detected.
• the expression level of the biomarker is detected by forming a complex between the biomarker and a labeled probe or primer.
• the biomarker is RNA or cDNA
• the RNA or cDNA can be detected by forming a complex between the RNA or cDNA and a labeled nucleic acid probe or primer.
• the biomarker is protein
• the protein can be detected by forming a complex between the protein and a labeled antibody.
• the label is a fluorescent label.
• the test sample is blood, serum, plasma, or neoplastic tissue.
• the first predetermined cutoff value can be derived from a plurality of reference samples obtained from subjects free of a neoplastic disease.
• the second predetermined cutoff value can be derived from a plurality of reference samples obtained from subjects whose melanomas are being adequately controlled by therapies like immune therapy.
• the third predetermined cutoff value can be derived from a plurality of reference samples obtained from subjects whose melanomas have been completely removed by surgery and they are considered "disease free.”
• each reference sample can be blood, serum, plasma, or non-neoplastic ti sue.
• the subject in need thereof is a subject diagnosed with a melanoma, a subject having at least one melanoma symptom, or a subject having a predisposition or familial history for developing a melanoma.
• the subject is a human.
• the algorithm is XGB, RF, glmnet, cforest, CART, treebag, knn, nnet, SVM-radial, SVM-linear, NB, NNET, or mlp.
• FIG. 1 is a graph showing vi sualization of the mel anoma score as a system of three contributors to the clinical picture - Control, Response, and Progression. Samples towards each of the corner represent pure representations of each clini cal group. Samples in the middle are in the area of both algorithmic and clinical uncertainty.
• FIG. 2 is a graph showing the metrics for the test in the test set ranged from 87-100%.
• FIGs. 3A-3C are a set of graphs showing the evaluation of the circulating melanoma gene test (Meianomx) in test set 2, Values were significantly higher in melanoma samples than in controls (FIG. 3 A). Patients who were responding to therapy had values similar to controls. Receiver operator curve analysis of test set 2 identifying the AUC for differentiating melanoma from controls was >0.95 (FIG. 3B). The metrics for the test ranged from 78-92% (FIG. 3C),
• FIGs. 4A-4B are a set of graphs showing the effect of surgery on the Meianomx. Levels were significantly decreased by surgeiy ( 0.0001) (FIG. 4A), Values in the NED (no evidence of disease after surgeiy) group were significantly lower than in those with residual disease after surgery (p 0.0007 ) (FIG. 4B).
• FIG . 5 is a graph showing the effect of therapy on the Meianomx score. Levels were significantly decreased by immunotherapy (ipilimumab) or a BRAF inhibitor (Vemurafenib).
• FIGs. 6A-6B are a set of graphs showing Meianomx score in 3 different melanoma cell lines.
• FIG. 6A identifies the cell lines demonstrate elevated expression - Meianomx score ranging from 40 (A375) to 95 (Hs294).
• FIG. 6B identifies that spiking these cells into blood from a subject that does not have a melanoma, resulted in detectable gene expression and scores. A minimum of 1 ceil/mi of blood could be consistently identified.
• FIGs. 7A-7B are a set of graphs showing expression in tumor tissue and its correlation with blood samples collected at the same time.
• the Meianomx score ranged 40-97 in melanoma tumor tissue.
• normal epithelium exhibited values ⁇ 20.
• FIG, 7B gene expression in tumor tissue is compared to matched blood samples. This is highly concordant (correlation -0.80).
• melanoma Early signs of melanoma include changes to the shape or color of existing moles or, in the case of nodular melanoma, the appearance of a new lump anywhere on the skin. At later stages, the mole may itch, ulcerate or bleed. Visual inspection is the most common diagnostic technique.
• Melanoma can be divided into the following types: lentigo maligna, lentigo maligna melanoma, superficial spreading melanoma, acrai lentiginous melanoma, mucosal melanoma, nodular melanoma, polypoid melanoma, and desmoplastic melanoma,
• RNA can be isolated from the peripheral blood of patients with melanoma. This expression profile is evaluated in an algorithm and converted to an output (prediction). It can identify active disease, provide an assessment of treatment responses, or predict risk of relapse, in conjunction with standard clinical assessment and imaging.
• the present disclosure provides a method for detecting a melanoma in a subject in need thereof, including: (1) determining the expression level of at least 29 biomarkers from a test sample from the subject by contacting the test sample with a plurality of agents specific to detect the expression of the at least 29 biomarkers, wherein the at least 29 biomarkers comprise ATLl, ATP6V0D, C10RF21, CFLAR, CFLAR-AS l, CHPl, DDX55, DMD,
• the at least one housekeeping gene is selected from the group consisting of ALG9, SEPN, YWHAQ, VP S37 A, PRRC2B, DOPEY2, NDUFBI l, ND4, MRPL19, PSMC4, SF3A1, PUM l, ACTB, GAPD, GUSB, RPLP0, TFRC, MORF4L1, 18S, PPIA, PGK1, RPL13A, B2M, YWHAZ, SDHA, HPRT1, TOX4, and TPT1.
• the at least one housekeeping gene comprises TOX4 and TPT1 .
• the normalized expression level is obtained by: (1 ) normalizing the expression level of each of ATL1, ATP6V0D, C 10RF21, CFLAR, CFLAR-AS l, CHPl, DDX55, DMD, DNAJC9, ENOSFl , FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC64647I, LOH12CR, PBXIP1, RNF5, SERTAD2,
• the provided methods are those that are able to classify or detect a melanoma.
• the provided methods can identify or classify a melanoma in a human blood sample.
• the methods can provide such information with a specificity, sensitivity, and/or accuracy of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
• the agents can be any agents for detection of the biomarkers, and typically are isolated polynucleotides or isolated polypeptides or proteins, such as antibodies, for example, those that specifically hybridize to or bind to the at least 29 biomarkers.
• the biomarker can be RNA, cDNA, or protein.
• the RNA can be reverse transcribed to produce cDNA (such as by RT-PCR), and the produced cDNA expression level is detected.
• the expression level of the biomarker can be detected by forming a complex between the biomarker and a labeled probe or primer.
• the biomarker is RNA or cDNA
• the RNA or cDNA detected by forming a complex between the RNA or cDNA and a labeled nucleic acid probe or primer.
• the complex between the RNA or cDNA and the labeled nucleic acid probe or primer can be a hybridization complex.
• the protein can be detected by forming a complex between the protein and a labeled antibody.
• the label can be any label, for example a fluorescent label, chemiluminescence label, radioactive label, etc.
• the protein level can be measured by methods including, but not limited to, immunoprecipitation, ELISA, Western blot analysis, or immunohistochemistry using an agent, e.g., an antibody, that specifically detects the protein encoded by the gene.
• the methods are performed by contacting the test sample with one of the provided agents, more typically with a plurality of the provided agents, for example, a set of polynucleotides that specifically bind to the at least 29 biomarkers.
• the set of polynucleotides includes DNA, RNA, cDNA, PNA, genomic DNA, or synthetic oligonucleotides.
• the methods include the step of isolating RNA from the test sample prior to detection, such as by RT-PCR, e.g., QPCR.
• detection of the melanoma biomarkers, such as expression levels thereof includes detecting the presence, absence, or amount of RNA.
• the RN A is detected by PGR or by hybridization.
• the polynucleotides include sense and antisense primers, such as a pair of primers that is specific to each of the at least 29 biomarkers.
• the detection of the at least 29 biomarkers is carried out by PCR, typically quantitative or real-time PCR.
• detection is carried out by producing cDNA from the test sample by reverse transcription; then amplifying the cDNA using the pairs of sense and antisense primers that specifically hybridize to the panel of at least 28 biomarkers, and detecting products of the amplification.
• the test sample can be any biological fluid obtained from the subject.
• the test sample is blood, serum, plasma or neoplastic tissue.
• the first predetermined cutoff value can be derived from a plurality of reference samples obtained from subjects free of a neoplastic disease.
• Each reference sample can be any biological fluid obtained from a subject not having, showing symptoms of or diagnosed with a neoplastic disease.
• the reference sample is blood, serum, plasma, or nonneoplastic tissue.
• the subject in need thereof can be a subject diagnosed with a melanoma, a subject having at least one melanoma symptom or a subject having a predisposition or familial history for developing a melanoma.
• the subject can be any mammal.
• the subject is human.
• the terms "subject” and “patient” are used interchangeably herein. I
• the score is the Melanomx score, which has a scale of 0 to 100.
• the Melanomx score is the product of a classifier built from predictive classification algorithms, e.g., XGB, RF, gimnet, cforest, CART, treebag, knn, nnet, SVM-radial, SVM-linear, NB, NNET, or mlp.
• the algorithm analyzes the data (i.e., expression levels) and then assigns a score.
• the method can further include treating the subject identified as having a melanoma with surgery, drug therapy, radiation therapy, or a combination thereof.
• the drug therapy can be an immunotherapy, a targeted therapy, a chemotherapy, or a combination thereof.
• the drug therapy includes an immunotherapy.
• immunotherapies for treating a melanoma include, but are not limited to, Imlygic (T-VEC), Yervoy in combination with Opdivo, Opdivo (nivolumab), Keytruda (pembrolizumab), Yervoy (ipilimumab),
• the drug therapy includes a targeted therapy such as a BRAF inhibitor.
• targeted therapies for treating a melanoma include, but are not limited to, Zelboraf in combination with Coteilic (cobimetinib), Tafmlar in combination with Mekinist, Tafmlar (dabrafenib), Mekinist (trametinib), and Zelboraf (vemurafenib).
• the drag therapy includes a chemotherapy.
• the chemotherapy includes dacarbazine.
• the present disclosure also provides a method for determining whether a melanoma in a subject is stable or progressive, including: (1) determining the expression level of at least 29 biomarkers from a test sample from the subject by contacting the test sample with a plurality of agents specific to detect the expression of the at least 29 biomarkers, wherein the at least 29 biomarkers comprise ATL l, ATP6V0D, C 10RF21, CFLAR, CFLAR-AS l, CHP1, DDX55, DM! DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1 , 11.2 A.
• IQGAPl LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, YY2, and at least one housekeeping gene; (2) normalizing the expression level of each of ATLl , ATP6V0D, C 10RF21 , CFLAR, CFLAR-ASl, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAPl , LOC494127, LOC646471, LOH12CR, PBXIPl , RNF5, SERTAD2,
• the second predetermined cutoff value can be derived from a plurality of reference samples obtained from subjects whose melanomas are being adequately controlled by therapies like immune therapy,
• Surgical resection is a procedure that removes melanoma tissues from the subject in need thereof.
• the present disclosure also provides a method for evaluating the extent of surgical resection in a subject having a melanoma, including: (1) determining the expression level of at least 29 biomarkers from a test sample from the subject after the surgical resection by contacting the test sample with a plurality of agents specific to detect the expression of the at least 29 biomarkers, wherein the at least 29 biomarkers comprise ATL1 , ATP6V0D, C10RF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA- DRA, HNRNPA3P1 , IL23A, IQGAP1 , LOC494127, LOC646471 , LOH12CR, PBXIPl, RNF5, SERTAD2, SLC35G5, SPATS2L,
• the third predetermined cutoff value can be derived from a plurality of reference samples obtained from subjects whose melanoma disease has been completely removed by surgery and they are considered "disease free,"
• the report further identifies that the risk of melanoma recurrence is high when the normalized expression level is equal to or greater than the third predetermined cutoff value or identifies that the risk of melanoma recurrence is low when the normalized expression level is below the third predetermined cutoff value.
• the present disclosure also provides a method for determining a response by a subject having a melanoma to a therapy, comprising: (1) determining a first expression level of at least 28 biomarkers from a first test sample from the subject at a first time point by contacting the first test sample with a plurality of agents specific to detect the expression of the at least 28 biomarkers, wherein the 28 biomarkers comprise ATLl, ATP6V0D, C10RF21, CFLAR, CFLAR-ASl, CHPl, DDX55, DMD, DNAJC9, ENOSFl, FANCL, HJURP, HLA-DOA, HLA- DRA, HNRNPA3P1, ⁇ .23 ⁇ .
• the methods can predict treatment responsiveness to, or determine whether a patient has become clinically stable following, or is responsive or non- responsive to, a melanoma treatment, such as a surgical intervention or drug therapy (for example, an immunotherapy or targeted therapy).
• a melanoma treatment such as a surgical intervention or drug therapy (for example, an immunotherapy or targeted therapy).
• the methods can do so with a specificity, sensitivity, and/or accuracy of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
• it can differentiate between treated and untreated melanoma with a specificity, sensitivity, and/or accuracy of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%,
• the first and second test samples can be of the same type. In some embodiments, the first and second test samples can be of different types.
• the therapy can be a drug therapy.
• the drug therapy can be an immunotherapy, a targeted therapy, a chemotherapy, or a combination thereof.
• the therapy can be a radiation therapy.
• the fi rst time point is prior to the administration of the therapy to the subject.
• the first time point is after the administration of the therapy to the subject.
• the second time point can be a few days, a few weeks, or a few months after the first time point.
• the second time point can be at least 1 day, at least 7 days, at least 1 days, at least 30 days, at least 60 days, or at least 90 days after the first time point.
• the second expression level is significantly decreased as compared to the first expression level when the second expression level is at least 0% less than the first expression level. In some embodiments, the second expression level is significantly decreased as compared to the first expression level when the second expression level is at least 20% less than the first expression level. In some embodiments, the second expression level is significantly decreased as compared to the first expression level when the second expression level is at least 30% less than the first expression level. In some embodiments, the second expression level is significantly decreased as compared to the first expression level when the second expression level is at least 40% less than the first expression level. In some embodiments, the second expression level is significantly decreased as compared to the first expression level when the second expression level is at least 50% less than the first expression level.
• the second expression level is significantly decreased as compared to the first expression level when the second expression level is at least 60% less than the first expression level. In some embodiments, the second expression level is significantly decreased as compared to the first expression level when the second expression level is at least 70% less than the first expression level. In some embodiments, the second expression level is significantly decreased as compared to the first expression level when the second expression level is at least 80% less than the first expression level . In some embodiments, the second expression level is significantly decreased as compared to the first expression level when the second expression level is at least 90% less than the first expression level.
• the method further comprises determining a third expression level of the at least 28 biomarkers from a third test sample from the subject at a third time point by contacting the third test sample with a plurality of agents specific to detect the expression of the at least 28 biomarkers, wherein the third time point is after the second time point.
• the method can further comprise creating a plot showing the trend of the expression level change.
• the present disclosure also provides an assay comprising: (1) determining the expression level of biomarkers consisting essentially of the following 30 biomarkers from a test sample from a patient diagnosed of a melanoma or a subject suspected of having a melanoma: ATLl, ATP6V0D, C10RF21, CFLAR, CFLAR-AS 1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3PI , IL23A, IQGAP1 ,
• RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2 to the expression level of TOX4, thereby obtaining a first normalized expression level of each of ATLl, ATP6V0D, C10RF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSFl, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, 11.23 A, IQGAPl, LOC494127, LOC646471 , LOH12CR, PBXIPl, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2; (3) normalizing the expression level of each of ATLl , ATP6V0D, C10RF21 , CFLAR, CFLAR- AS1, CHP1, DDX55, DMD, DNAJC9, ENOSFl, FANCL,
• RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2 to the expression level of TPTI, thereby obtaining a second normalized expression level of each of ATLl, ATP6V0D, C10RF21 , CFLAR, CFLAR-AS1, CHP1 , DDX55, DMD, DNAJC9, ENOSFl , FANCL, HJURP, HLA-DOA, HLA-DRA, HNR PA3P1, IL23A, IQGAPl, LOC494127, LOC646471, LOH12CR, PBXIPl , RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2; (4) averaging the first normalized expression level and the second normalized expression level to obtain a normalized expression level for each biomarker; (5) inputting each normalized expression level into an algorithm to generate a score; and (6) comparing the
• the present disclosure also provides an assay comprising: (1) determining the expression level of biomarkers consisting of the following 30 biomarkers from a test sample from a patient diagnosed of a melanoma or a subject suspected of having a melanoma: ATLl , ATP6V0D, C10RF21 , CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSFl, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAPl , LOC494127, LOC646471 , LOH12CR, PBXIPl, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, YY2, TOX4, and TPTI, wherein the expression level is measured by contacting the test sample with a plurality of agents specific to detect the expression of the 30 biomarkers; (2) normalizing the expression level
• AAAGAACATC CACAGAATAG ACCTGAAGAC AAAAATCCAG AAGTACAAGC AGTCTGTTCA AGGAGCAGGG ACAAGTTACA GGAATGTTCT CCAAGCAGCA ATCCAAAAGA GTCTCAAGGA TCCTTCAAAT AACTTCAGGC TCCATAATGG GAGAAGTAAA GAACAAAGAC TTAAGGAACA GCTTGGCGCT CAACAAGAAC CAGTGAAGAA ATCCATTCAG GAATCAGAAG CTTTTTTGCC TCAGAGCATA CCTGAAGAGA GATACAAGAT GAAGAGCAAG CCCCTAGGAA TCTGC CTGAT AATCGATTGC ATTGGCAATG AGACAGAGCT TCTTCGAGAC ACCTTCACTT CCCTGGGCTA TGAAGTCCAG AAATTCTTGC ATCTCAGTAT GCATGGTATA TCCCAGATTC TTGGCCAATT TGCCTGTATG CCCGAGCACC GAGACTACGA CAGCTTTGTGTGTGTCCTGG TGAGCCGAGG
• Ci Ci ' TiTGGATT GCTGCTTGGA GAACATTCCT GTAACTTGTC
• TGCCCTCTCC CTTCTTGACC CCTAGCCCTT CCTTCCCTCC 4 CTCCTTCCCT CCTGTCGCCG TCTCTTCTGG CGCCGCTGCT
• AATAATGGAA TTTTTCTCTT CAAAGGTCCT TAGGTGTAAA TTGATGC CAT CGTAGGCAAG GTGCAGGCAG GATTTGAAGG CAAAAGTCAA TTCAGCTCTT GAGAAAAGGT GTCTTTC CAG CCTGAATTTT TCAGATTGAC TAGACCAAGC AGAATCTCTC AACCTGATCT TAGTATTTCC TAGAAAGCAC TTGACATTGT GTGAGGTCTC ACCTGAAGGA ACTTGGTGGT GACATTTGGG AGGGTGGAGG GAGGCAGTGT CCTTCCTGAC AGCACTTGCC TC C ATGGATC TTCTG ' TAC AC AG A CTCTTA TCTAGG ATGT
• GGTTCTGTTC ATGCTGCTTT CTGCGATGTG CGTGTCTGTT AGAATAGGCT CTCTACCCAG CTAGAACACC TTCCAGACAC TTGCTGGACA GCTATCTTCC ACATACTTCC CAGTITACAT TTGGTCTTAA TGATCTTGAA TAGATCCTCT CTTCATTTTA CTCAGCCAGG TTTTGTACTG ATGTACAGGT GTTAAATTAC TTCAAGCATT TTTGTAAGAG GTGTATATAA TTCAATAAAA AAGGTAAAAC ATGATGATTA AGTTCTGGGG GCTTTGTAAA TGATCCCACT AAAATGTGAC CTAGGAAAAA TATGAATGGT
• TGTTA C A AAA TTTG A AAGAT GTTTTAG A AA TTG ATTTTC C AGCTCGTGCT ATCCTGGAAA AATCTGATTT TACTATGGAT TGTGGAATTT GTTATGCTTA TCAACTTGAC GGTACCATTC CTGATCAAGT GTGTGATAAT TCTCAGTGTG GACAACCTTT CCATCAAATA TGCTTATATG AGTGGCTGAG AGGACTACTA ACTAGTAGAC AGAGTTTTAA CATCATATTT GGTGAATGTC CATATTGTAG TAAGC C A ATT ACCTTAAAAA TGTCTGGAAG GAAACACTGA AATAAGAATA CAACATTTCG GTGAAGAGCT GGAAACTTAA AAAATTATCA AAAGGAATTT TGGTATCATC TTCAGAGAAA AAATAAAGCA AGAAATACTA ACATCAAAAG GACAGGTATG ATGATGCG A T AATAATAAAC ATCTGCGTTT GTCTCTTCAC TAAGTAAA CTGGGAAATT GTAG
• GATACTGCAG TCGTATCTCC AGAAAGAGTC CTGGTGACCC AGCGAAACCA GCTTCATCTC CCAGAGAATG GGATC CTTTG CATCCTTCCT CCACAGACAT GGCCTTAGTA CCTAGAAATG ACAGCCTCTC CCTACAAGAG ACCAGTAGCA GCAGCTTCTT
• CTCTAGGGGC AAGTCAACTC TCAATTATAG TGAGGGCAGG TTCCCCAGTT GCCAGCCTAC ACCCTGGCCA GCCACCCAAG GGAATACCTG CTGCTGCTAA GGCAGTCAAT GTTGGGAGGG TCAGGGAAGG GGAGAGGAAG TAGCTGAGTG TAGAGATTAT CCAGGCTTTC CTTCCCGTCC TCTGTA CAG
• GCTGCAGACC CCCGTGTTGC CCAGTGACCT CCTGAGTTGG AGTTGTGTGG GGGC AGAGGG GATCCTCGCC TTGGTCTCCT TCACATGTGT GGGCTATGCG GTCACCAAGG CCCACCCTGC CCTGGTGTGC GCTGTCCTGC ATTC CGAGGT GGTTGTGGCC CTTATACTGC AGTATTATAT GCTCCATGAG ACTGTGGCAC
• TTAAACTCTA ATCTCTCTAC TTAATGCACA GTAGTCAGAT TATTCTCTTA AACATTTGCC TAGTAGAGGT TAAAATAGTT TAATCCTTAT GAAGATGGAA TAACTTCAAA CTCACATTGT GGCACTTAGA TCTTCCACCA AGACTTCATC CGTGAAATCC ACACCTCCCT GTTGGGTTCC CAATTACATT CCAAATTTAC ATTTCTTTTG AGAATCTCTG CATACTCCAG CTCTGTCCTG TTGATCCTAT TCTAGAAGTG CTTAATGCAG CAAGACACAG AAAGTTAAAC GCAAATTGCT GCAAAATTCA CCCTCAGTGG AGGACTAGAA ACACAACATG TCCAATTTAA AGCTCAGTTC ACAAGCAGTT CAATTCTGCT GGCATCAGAA AAGGAGATTC TAATTAAACA TTCTTAGGGA AGGACATCAA ATGAGGTTAA TGGGAAACGT TACCAGATTA AAAGCAGTTT TTTG
• GTCAGTAACT TAACTGCAAA CTAAACTGGT GATAATTATG GTAAAATTGC AAGACGAGCA ATAAATCTCA ACCAACTTGA GAGAACACTG ATAA ⁇ NM 0 GATTTCAGTT GAAAGATGTG TTTTTGTGAG TAGAGCACCG 29
• CTGTATTTTA CTATGAGATT CTTAATAACC CAGAGCTTGC
• TGGCTCCTGT ATCACATACG GAGGTCTTGT GTATCTGTGG CAACAGGGAG TTTCCTTATT CACTCTTTAT TTGCTGCTGT TTAAGTTGCC AACCTCCCCT CCCAATAAAA ATTCACTTAC ACCTCCTGCC TTTGTAGTTC TGGTATTCAC TTTACTATGT GATAGAAGTA GCATGTTGCT GCCAGAATAC AAGCATTGCT TTTGGCAAAT TAAAGTGCAT GTCATTTCTT AATACACTAG AAAGGGGAAA TAAATTAAAG TACACAAGTC CAAGTCTAAA ACTTTAGTAC TTTTCCATGC AGATTTGTGC ACATGTGAGA GGGTGTCCAG TiTGTCTAGT GATTGTTATT TAGAGAGTTG
• VPS 37 A 011451 TGTTTATCCA CCAATACGAC ATCACTTAAT GGATAAACAA
• TCCTAGAAAC CAGGTAGGTG TATCCCATAA CAAGGGAGGA GCATACCACA GCCCCTCATT TGATTAATTC ATTTGATCTA TCTATGTTAT TAAGTACCTA CTAGGAATAA GGCATTGTGG AAATACTATA CAAAGATAAA CATTGTTTAG ATGCTTATCT ACTTTCCTTT TCACCAGAAA AACAGAAAAA AAAGAAACAT TTTCTTACAG AGTAAAAATG TTCTACATAA TCACATGAGT AGTTCATCTC AGTGTTTTTT ATTCTTTAAA GTTGAACTAT CCCAGTTTCA TTCTATACCA TTCATTGGAT AACCTTGTTA CAACCCAGTC ATGAAACAGA GCAGTGTGAT CAGTTATCTG CATTTAACAA ATAGACAAAT CAGTTTACAT AAAGGTTATG TATGTCACCC ACGATGAAAA GAATCTGCAT TTGAATATGC C CGTATG A AT GTGGGTTCTG TTTTTGC A AC A GAG ATTAAG TG
• TCCC AC AGTG CCTGGCCCAG AAGCCTTGCT AAATATTTGA 36 ACAGGATTGC C C AATACTTT TCTGCTGTGA GAATGTAAGA TGGATCCAGA AGAGCAGGAG CTCTTAAATG ATTACAGATA CAGAAGCTAC TCTTCAGTGA TTGAAAAGGC TITGAGAAAT TTTGAGTCCT CGAGTGAATG GGCGGATCTC ATATCTTCAC TTGGCAAACT CAACAAGGCT CTTCAGAGTA ACCTGAGGTA CTCCTTGTTG CCAAGACGGC TCCTCATCAG CAAAAGATTA GCTCAGTGTT TGCACCCTGC CCTGCCCAGT GGTCCACT
• TTGTATTTAT CAGCTTGCAA ATTCTTGGAC ACAGCGCTTT CTTTTCCACC TGACAAGATG CCATTATTTC AAATTTATAG GTGGGCA l i 1 ATTCCAGAAG TGGACACAGA GGGCCCTGCC
• AAAAAAATCC AACTCTGCTT TTGGTCTTGC TTCTATAAAT ATATAGTGTA TACTTGGTGT AGACTTTGCA TATATACAAA TTTGTAGTAT TTTCTTGTTT TGATGTCTAA TCTGTATCTA TAATGTACCC TAGTAGTCGA ACATACHTi GATTGTACAA
• GCGTCTGCGG CATTTTGTCG GCTGGGTGTG GTACGAACGG GAGGTGATCC TGCCGGAGCG ATGGACCCAG GACCTGCGCA CAAGAGTGGT GCTGAGGATT GGCAGTGCCC ATTCCTATGC CATCGTGTGG GTGAATGGGG TCGAC ACGCT AGAGCATGAG GGGGGCTACC TCCCCTTCGA GGCCGACATC AGC AACCTGG
• ATCTCTTGCA CTCAAAGCTT GTTAAGATAG TTAAGCGTGC ATAAGTTAAC TTCCAATTTA CATACTCTGC TTAGAATTTG GGGGAAAATT TAGAAATATA ATTGACAGGA TTATTGGAAA TTTGTTATAA TGAATGAAAC ATTTTGTCAT ATAAGATTCA TATTTACTTC TTATACATTT GATAAAGTAA GGCATGGTTG TGGTTAATCT GGTTTATTTT TGTTCCACAA GTTAAATAAA TCATAAAACT TGATGTGTTA TCTCTTA
• nucleic acid molecule As used herein, the terms “polynucleotide” and “nucleic acid molecule” are used interchangeably to mean a polymeric form of nucleotides of at least 10 bases or base pairs in length, either ribonucleotides or deoxvnucleotides or a modified form of either type of nucleotide, and is meant to include single and double stranded forms of DNA.
• a nucleic acid molecule or nucleic acid sequence that serves as a probe in a microarray analysis preferably comprises a chain of nucleotides, more preferably DNA and/or RNA.
• a nucleic acid molecule or nucleic acid sequence comprises other kinds of nucleic acid structures such a for instance a DNA/RNA helix, peptide nucleic acid (PNA), locked nucleic acid (LNA) and/or a ribozyme.
• PNA peptide nucleic acid
• LNA locked nucleic acid
• nucleic acid molecule ' also encompasses a chain comprising non-natural nucleotides, modified nucleotides and/or non- nucleotide building blocks which exhibit the same function as natural nucleotides.
• hybridize As used herein, the terms “hybridize,” “hybridizing”, “hybridizes,” and the like, used in the context of polynucleotides, are meant to refer to conventional hybridization conditions, such as hybridization in 50% formamide/6XSSC/0.1% SDS/100 ⁇ g/ml ssDNA, in which temperatures for hybridization are above 37 degrees and temperatures for washing in 0.1 XSSC/0.1% SDS are above 55 degrees C, and preferably to stringent hybridization conditions.
• the term "normalization” or “normalizer” refers to the expression of a differential value in terms of a standard value to adjust for effects which arise from technical variation due to sample handling, sample preparation, and measurement methods rather than biological variation of biomarker concentration in a sample.
• the absolute value for the expression of the protein can be expressed in terms of an absolute value for the expression of a standard protein that is substantially constant in expression.
• diagnosis also encompass the terms “prognosis” and “prognostics”, respectively, as well as the applications of such procedures over two or more time points to monitor the diagnosis and/or prognosis over time, and statistical modeling based thereupon.
• diagnosis includes: a. prediction (determining if a patient will likely develop aggressive disease (hyperproliferative/invasive)), b. prognosis (predicting whether a patient will likely have a better or worse outcome at a pre-seiected time in the future), c.
• therapy selection d. therapeutic drug monitoring, and e, relapse monitoring
• providing refers to directly or indirectly obtaining the biological sample from a subject.
• providing may refer to the act of directly obtaining the biological sample from a subject (e.g., by a blood draw, tissue biopsy, lavage and the like).
• providing may refer to the act of indirectly obtaining the biological sample.
• providing may refer to the act of a laboratory receiving the sample from the party that directly obtained the sample, or to the act of obtaining the sample from an archive.
• TP true positives
• TN true negatives
• FP false negatives
• FN false negatives
• biological sample refers to any sample of biological origin potentially containing one or more biomarkers.
• biological samples include tissue, organs, or bodily fluids such as whole blood, plasma, serum, tissue, lavage or any other specimen used for detection of disease,
• subject refers to a mammal, preferably a human.
• Treating or “treatment” as used herein with regard to a condition may refer to preventing the condition, slowing the onset or rate of development of the condition, reducing the risk of developing the condition, preventing or delaying the development of symptoms associated with the condition, reducing or ending symptoms associated with the condition, generating a complete or partial regression of the condition, or some combination thereof,
• Biomarker levels may change due to treatment of the disease.
• the changes in biomarker levels may be measured by the present disclosure. Changes in biomarker levels may be used to monitor the progression of disease or therapy.
• “Altered”, “changed” or “significantly different” refer to a detectable change or difference from a reasonably comparable state, profile, measurement, or the like. Such changes may be all or none. They may be incremental and need not be linear. They may be by orders of magnitude. A change may be an increase or decrease by 1%, 5%, 10%, 20%,30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 00%, or more, or any value in between 0% and 100%.
• the change may be 1-fold, 1.5- fold, 2-fold, 3-fold, 4-fold, 5-fold or more, or any values in between 1-fold and five-fold.
• the change may be statistically significant with a p value of 0.1, 0.05, 0.001, or 0.0001.
• stable disease refers to a diagnosis for the presence of a melanoma, however the melanoma has been treated and remains in a stable condition, i.e. one that that is not progressive, as determined by imaging data and/or best clinical judgment.
• progressive disease refers to a diagnosis for the presence of a highly active state of a melanoma, i.e. one has not been treated and is not stable or has been treated and has not responded to therapy, or has been treated and active disease remains, as determined by imaging data and/or best clinical judgment.
• each algorithm produced probability scores that predicted the sample.
• Each probability score reflects the "certainty" of an algorithm that an unknown sample belongs to Control, Responder/Stable or Progressive class.
• FANCL complementation group L [2pl6.1] 90 636.1 138 540 6-7
• HLA-DRA complex class 11, DR alpha [6p21.32] 48 4 129 884 4-5
• the data for the utility of the test to differentiate melanoma from controls are included in Table 3.
• the metrics are included in FIG. 2, These are: sensitivity >90%, specificity 100%, PPV 100%, NPV 87%.
• the overall accuracy is 94%. The tool can therefore differentiate between controls and aggressive and stable melanoma disease.
• Table 3 Confusion matrix showing classification accuracy of the 5-model algorithm that determines whether a sample is a melanoma or a control in blood samples
• test set 2 The mean Melanomx score in the melanoma group was 73 ⁇ 31 versus 10 ⁇ 8 in the control group (FIG. 3A).
• the receiver operator curve analysis demonstrated the score exhibited an area under the curve (AUG) of 0.96 (FIG. 3B) and the metrics were 88-100% (FIG. 3C).
• melanoma was the source for the blood-based gene expression assay by evaluating expression in different melanoma cell lines, in tumor tissue and by comparing expression in blood with tumor tissue collected at the same time-point during surgery.
• Kidd M, Drozdov I, Modlin I Blood and tissue neuroendocrine tumor gene cluster analysis correlate, define hallmarks and predict disease status. Endocr Relat Cancer. 2015;22: 561 -575. doi: 510.1530/ERC-1515-0092. Epub 2015 Jun 1532.

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